Data-Driven Based In-Depth Interpretation and Inverse Design of Anaerobic Digestion for CH₄-Rich Biogas Production

Anaerobic digestion (AD) is one of the most widely used bioconversion technologies for renewable energy production from wet biowaste. However, such an AD system is so complicated that it is challenging to fully comprehend this process and design the operational conditions for a specific biowaste to achieve CH₄-rich biogas. In this context, ensemble machine learning (ML) algorithms were employed to develop multitask models for jointly predicting the CH₄ yield and content in biogas and understanding this complicated process. Based on the best ensemble model with the R² values of 0.82 and 0.86 for the multitask prediction of CH₄ yield and content, the top three critical factors for CH₄ yield/contents were identified and their interactions with process acid generation and microbial community in the AD process were comprehensively interpreted to unveil their importance on CH₄ generation. Moreover, the well-developed ensemble model was integrated with an optimization algorithm to inversely design the AD process for a real-world food waste, in which the CH₄ yield was as high as 468.7 mL/gVS and the calculation results were experimentally validated with relative errors of 9–16%. This work provides a creative approach to gain insights and inverse design for AD reactors, which is helpful to waste-to-energy technologists and practitioners.